RECENT DEBATES ON DATA BASE FOR

MEASUREMENT OF POVERTY IN INDIA:

SOME FRESH EVIDENCE*

by

K. Sundaram

Suresh D. Tendulkar**

Delhi School of Economics University of Delhi

Delhi 110 007, India

December 2001

* Paper prepared for the Workshop on Poverty Monitoring and Evaluation jointly organised by the World Bank and Planning Commission, New Delhi, January 11-12, 2002. ** We are grateful to Ms. Shilpa Bogra and Mr. Sanjeev Sharma, Senior Programmer, DSE-CDE Computer Centre for excellent programming support and to Ms. Anjali Gautam for efficient typing.

I. Introduction

In India, the discussions on poverty have focused on one measure namely,

proportion of the population below an exogenously specified poverty line (or headcount

ratio). While non-income dimensions like health and education have been recognised,

the heated debates have always revolved around changes in headcount ratio measure of

poverty over time and across space.

The computation of headcount ratio involves three basic ingredients:

a. the specification of the poverty line in terms of irreducible minimum

monthly per capita total (household consumer) expenditure (pcte);

b. the 'deflator' for adjusting the poverty line from the original price-base to

the year in which headcount ratio is to be calculated;

c. the size-distribution of pcte which is provided by National Sample

Surveys (NSS) on Household Consumer Expenditure (HCE).

The present paper does not deal with the debates relating to (a) and (b) but

concentrates only on (c). In particular it takes up two major issues:

The first is the downward bias in the NSS-based estimate of household consumer

expenditure and the resulting over-statement of headcount ratio. It has been argued that

NSS-based estimates of HCE have been consistently lower than those emerging from the

private final consumption expenditure (PFCE) from national accounts statistics (NAS)

and that the divergence had been growing in the 1990s. So that headcount ratios, based

on NSS have been increasingly overstating poverty. Sen (2000) who examined the NAS

based PFCE and NSS-based HCE (in per capita terms) from the NSS 27th round (1972-

73) to the 53rd round (1997) had shown clearly that 'there is no evidence of any large

widening of the gap between the NAS and NSS estimates nominal consumption during

the 90s". (p.4509)

Taking Sen's conclusion as given, in this paper, following Minhas and Kansal

(1990), we emphasize (Section II.1) the continued 'fluidity' in NAS in the sense of the

estimates being inherently subject to repeatitive revisions. For this purpose, we undertake

inter-se comparison PFCE estimates for the same year 1993-94 in the three consecutive

publications of NAS 1998, NAS 1999 and NAS 2000. The point is that NAS-based

PFCE does not provide any independent and firm yardstick for adjusting the NSS-based

estimates. This does not preclude the possibility of improving both NSS and NAS by

undertaking cross-validation to detect the weaknesses of each at the disaggregated level.

In this context we draw on the recent cross-validation exercise undertaken jointly by the

National Accounts Division of the Central Statistical Organisation and the Sample

Design and Research Division of the National Sample Survey Organisation. (Section

II.2).

Ruling out (a) the growing divergence between NAS and NSS and (b) the

desirability of adjusting NSS-based estimates on the basis of NAS, we turn in Section III

to an examination of inter se comparability across NSS rounds. After providing an

overview of the problems of comparability in Section III.1, we turn to an analysis

(Section III.2) based on unit level records the 50th (1993-94) and the 55th (1999-2000)

rounds of NSS. In particular (a) we examine the effects on the size distribution of pcte of

two alternative recall periods of 30-day and 365-days in the 50th round for certain

infrequently purchased items such as clothing, footwear, durables, education and

institutional health expenditure (Section III.3); (b) we analyse the effects on the size

distribution of pcte of the abridgment of the consumer expenditure schedule in the

employment-unemployment survey (EUES) of the 55th round (Section III.4); (c) we

undertake a comparison of per capita monthly consumer expenditure at the commodity-

group-level for the rural population between EUES-based abridged schedule and the

Consumer Expenditure Survey based detailed schedule (Section III.5). The idea in this

section is to throw new light on the 7-day-30-day recall controversy in respect of food,

paan, tobacco and intoxicants in the 55th round.

Our conclusion is that the Consumer Expenditure Survey (CES) has approximated

the 30-day recall and that, as a conseqeunce, the apprehension that 7-day recall may have

resulted in an overstatement of consusmer expenditure (in comparison with the 30-day

recall used in earlier rounds) and the understatement of resulting headcount ratio for

1999-2000 is not justified. The resulting headcount ratios for the rural, urban and total

(rural plus urban) population and their comparisons with 1993-94 and 1983 are presented

in the final Section III.6.

II. The NAS-NSS Comparison and Estimation of Poverty

In this section we will draw attention to the continued "fluidity" - to borrow that

elegant phrase from the work of Professors Minhas and Kansal on this issue more than a

decade ago - of the NAS estimates. Then, drawing on a recent NAD-SDRD study on

cross-validation of the two sets of estimates, try and isolate the impact of different

sources of divergence between the two. To depart from the usual practice, we will state

our conclusion of this section up front. This is the same as the one Professor Minhas

reached thirteen years ago and I can do no better then quote him in full.

"The results of this validation exercise should make it abundantly clear that it is

indeed hazardous to carry out pro-rata adjustment in the observed size-distribution of

consumer expenditure in a particular NSS round by multiplying it with a scalar derived

from the ratio between the NAS estimate of aggregate private consumption for the

nearest financial year and the total NSS consumer expenditure available from that

particular round of household budget survey. This kind of mindless tinkering with the

NSS size distribution of consumer expenditure, as practiced by the Planning Commission

in the Seventh Five Year Plan documents, does not seem permissible either in theory or

in the light of known facts". (Minhas, (1988), p.37). Even though the Planning

Commission itself has discontinued this practice, this "mindless tinkering" is still a

thriving industry - albeit a one-man enterprise"!

II.1 The Continued Fluidity of NAS estunates

In an earlier paper (Sundaram and Tendulkar, (2001), we had pointed out very

large revisions in the NAS estimates of private final consumption expenditure (PFCE) at

current prices for 1993-94 as between NAS 1998 and NAS 1999. We find that NAS

2000 has made further revisions to their estimates of PFCE for 1993-94 at 1993-94

prices. To get an idea of the extent of differences in the NAS estimates of PFCE for the

same year (1993-94) at current prices in three successive issues (1998, 1999 and 2000) of

National Accounts Statistics we present these estimates in the detail available in the

published documents. In addition, following Professors Minhas and Kansal (1990), we

present, for each broad item-group, as well as for the total PFCE, the sum of the absolute

differences (i.e. ignoring signs) between the estimates for two pairs of years (NAS 1999

relative to NAS 1998 and NAS 2000 relative to NAS 1999). These are presented in

columns (4) and (5) of Table II.1.

In the aggregate, the PFCE estimate for 1993-94 in NAS 1999 was higher than

that published in NAS 1998 by close to 14 percent. But this aggregate difference is a net

effect of increases for some items and decreases for others. If we aggregate the absolute

differences (ignoring signs) between the two estimates, the overall difference between the

two estimates is close to 25 percent. And of the 39 items/item groups distinguished in the

published documents, the absolute difference (as a percentage of the NAS 1998

estimates) was 5 percent or more in 26 cases, with this difference being 20 percent or

more in 14 out of these 26 cases, with a more than 300 percent difference in the case of

"other foods".

Given the fact that NAS 1999 was reporting the NAS estimates under the New

Series with 1993-94 as the base year while NAS 1998 was reporting the old series with

1980-81 base, by itself does not justify these large changes. For, both the estimates are

for the same year, 1993-94 and they are at 1993-94 prices - which are of course the

"current prices" for 1993-94. So that, ruling out price changes, the reported changes must

reflect the changes in the estimates of underlying quantities.

To understand these changes note that the NAS estimates of PFCE are

predominantly based on the commodity flow method1. As such, it requires firm data on:

1 In this method, private final consumption expenditure is derived as a residual after numerically specifying all the other components in ex post supply -demand identity for a given commodity/service group: Total supply equals domestic production plus imports Total demand equals intermediate consumption plus private final consumption expenditure plus government final consumption of expenditure

plus domestic capital formation plus exports

(i) domestic production; (ii) net exports; and changes in stocks which, when netted out

for intermediate uses will define availability for domestic consumption and capital

formation. From the portion available for domestic consumption, and after deducting the

use for capital formation, consumption by government and business has to be netted out

to derive, residually, the net quantities available for private consumption. Further, in

order that the valuation is appropriate, the portion consumed by households from home-

grown stock need to be separated from market purchases, so that data on marketed

surplus is also needed.

There are significant gaps and weaknesses of data base at virtually every step of

the procedure which are papered over by the use of a number of rates and ratios of

varying vintages.

As more current data become available, the NAS estimates also get modified and

at the time of the introduction of a New Series of NAS, a big-bang effort is made to bring

as much fresh evidence to bear on these estimates. So, at the time of a changeover of the

series, large changes are understandable. Yet, even with the introduction of the new

series with 1993-94 base, the reliance on rates and ratios remains substantial.

Less understandable is the need for a further set of revision between NAS 1999

and NAS 2000. Even though the changes in many sectors (in 25 of the 37 item-groups)

are less than 5 percent of the corresponding estimates in NAS 1999, in the remaining

twelve items/item-groups the difference is large enough to take the ratio of (the sum of)

absolute difference to aggregate PFCE as per NAS 1999 perilously close to the 5 percent

limit (4.99 percent to be exact). These 10 item/item-groups, (with the percentage

difference between the estimates in NAS 2000 and NAS 1999 to the estimate in NAS

1999 in parentheses) are:

plus changes in stocks

Sugar and gur (7.58); Meat, egg and fish (5.26); other foods (24.85); Tobacco &

Products (50.09); Hotels and Restaurants (13.32) in the Food, Beverages and Tobacco

group; clothing (17.95); footwear (6.47); furniture, furnishing and repair (10.01); other

goods in Furniture Furnishing Appliances and Services category (14.96);

Communications (7.66); Recreations and Cultural services (8.10); and Personal Care and

effect (89.25).

Revisions of fifty percent or more - Tobacco and Products and Personal Care and

effect - within one year is truly astonishing.

The case of revisions in the NAS estimates of clothing is also interesting. The

basic explanation given for the sharp drop in the NAS 1999 estimate relative to that given

in NAS 1998) was to bring these estimates in line with the underlying GDP estimates

rather than use the "independent" estimates from the Office of the Textile Commissioner.

So far so good. Why then raise the new estimates by 18 percent? There is another

interesting side light. Dr. Bhalla, an otherwise ardent advocate of aligning the NAS and

NSS estimates of private consumption expenditure prefers to use the NAS 1998 estimates

for clothing rather than the one aligned to the GDP estimates!

The point is not to argue that these revisions are necessarily unwarranted, nor

certainly to detract from the massive effort that goes into the preparation of a system of

National Accounts. Our idea in drawing attention to the continued fluidity of the NAS

estimates is to highlight the central problem: even with the new series, the currentness of

data base needed for the implementation of the NAS falls considerably short of what is

required for the NAS estimates to serve as an unqualified "touch stone" to test the

validity of the NSS estimates and much less for "mindless tinkering" of the NSS size-

distribution.

It is important to stress that by "currentness of data base" we mean data that

would reflect the actual flow of goods and services during the accounting year for which

the estimates are presented. So that, the use of the "latest" survey reports for updating

bench-mark values and/or for revising some of the rates and ratios used in the NAS

estimates still doesn't render them "current". The widespread use of a number of sample

survey based results (albeit the latest available) in the NAS estimates, brings to the fore

an important point made by Professor Minhas over a decade ago).

As Professor Minhas noted: "The national accounts data get their copious share of

sampling errors, not from one but many sample surveys from which the production data

base of the national accounts gets built up". (Minhas, 1988, p.14). This continues to be

the case even to-day.

II.2 NAS-NSS Comparison: Results from a Recent Cross-Validation Exercise

Recently the National Accounts Division (NAD) of the CSO and the Survey

Design and Research and Development (SDRD) division of the NSSO carried out a

detailed study aimed at Cross Validation of Private Consumption Expenditure Available

From Household Survey and National Accounts. (hereafter referred to as the NAD-

SDRD study). In this study, we have a comparison of the estimates of PFCE from NAS

2000 for 1993-94 with the estimates of household consumer expenditure based on the

NSS 50th Round Consumer Expenditure Survey. This is a major effort at making the

estimates comparable with close to 200 items being distinguished. Whenever feasible,

they have also presented the implicit unit values from the two sources and make

comparisons with and without adjustment for prices. In general, the adjustment for

prices, narrows the gap.

In discussing these results, let us first get out of the way the notional elements in

the NAS estimates that unnecessarily inflate the divergence between the two estimates:

imputed rent and financial intermediation services indirectly measured (FISIM). As per

NAS 2001, these items contributed Rs. 49,098 crores to the gross difference between the

two estimates of Rs.219,001 crores i.e. over 22 percent of the difference. Excluding

these items, the difference between the two estimates is Rs.169,903.

We next focus on the Food, Beverages and Tobacco group, where, in the net

(there are some items, notably non-alcoholic beverages, where the NSS estimates exceed

the NAS estimates) the NAS-estimates exceeds the NSS stimates by Rs.91,177 crores or

about 54 percent of the aggregate difference excluding the notional elements. This is also

the group which accounted for 76 and 72 percent of the expenditure of the bottom 30

percent of the population in, respectively, rural and urban India.

Effect of Adjustments for differences in Unit Values

In the Food, beverages and tobacco group, revaluing the NAS estimates of PFCE

at NSS-based unit values (which are mostly lower with pulses and products as the major

exception) reduces the NAS estimate by Rs.5835 crores. Correspondingly, without any

other adjustment, the excess of NAS PFCE estimate over the corresponding NSS based

estimate is also reduced by the same amount. It may be noted that this is a little over 3

percent of the aggregate difference between the two estimates (excluding the notional

elements)2. As a percentage of the difference in the food category, it would be 6.4

percent.

Effect of Assumptions about Intermediate Consumption

The NAS estimates of PFCE for a number of commodities in the Food group are

based on the assumption of zero use for intermediate consumption. The products/items

affected by this assumption and explicitly stated to be so are: pulses and products, milk

and milk products, vanaspati, chicken and eggs. Though not stated to be the case, the

same is also likely to be true for maida and fish. Also, why only vanaspati and not other

edible oils?

2 It is possible that the effect of the adjustment for the differences in unit values is somewhat greater than what is indicated in the text. The detailed tables in the NAD-SDRD study place the value of consumption of Tapioca and its products at Rs.1024 crores as against the NSS-based estimate of Rs.290 crores i.e. a difference of Rs.734 crores. It is possible that, as in the case of coarse cereals, the NAS unit values are higher (by about 16 percent). If so, the difference would come down by a further Rs.163 crores.

The excess of NAS PFCE estimates over the corresponding NSS-based estimates

in respect of pulses and products (Rs.769 crores), milk and milk products (Rs.10,977

crores), vanaspati (1790 crores), chicken and other birds (Rs.3589 crores) and eggs and

products (Rs.1341 crores) adds up to Rs.18,466 crores i.e. a little over 20 percent of the

difference between the two sources for food, beverages and tobacco taken as a group. To

this may be added the excess of NAS estimates over the NSS-based estimates in respect

of maida (Rs.1705 crores), and fish (Rs.3013 crores). In addition, we have the NAS

estimates of PFCE on oil seeds of Rs.3,508 crores as against the NSS-based estimates of

Rs.33 crores. Notice that the consumption of ground nuts as nuts is shown elsewhere as

part of the estimates of consumption of fruits and nuts. The clue here is the assumption

underlying the NAS estimates that "the entire amount of oilseeds retained by the

producers is consumed as oilseeds"! Apart from being prima facie, absurd, it also points

to the role of the implicit rates and ratios in respect of marketable produce.

In respect of sugar and gur, where the price-adjusted NAS estimate exceeds the

NSS-based estimate by close to Rs.10,000 crores, the NAS-assumption is that 5 percent

of the production used for intermediate consumption. As the NAS-SDRD puts it: "….it

appears that taking 5 percent of gur and sugar production as intermediate consumption is

unrealistic". In the case of sugar and gur, as Professor Minhas had noted in 1988, there is

the additional problem of not inconsiderable, unrecorded exports of sugar and gur across

the long and porus border.

Of course, it cannot be anybody's case that all of the observed difference between

the NAS and the NSS-based estimates in respect of the items listed above is due simply

to the assumption of zero (or low) use for intermediate consumption that underlies the

NAS estimates. But it is likely to have contributed substantially to the observed

divergence.

There is another important aspect of the NSS-NAS comparison that is relevant in

respect of the NSS-based estimates of household consumer expenditure on food,

beverages, paan, tobacco and intoxicants. Note that all the comparisons are being

conducted by reference to the NSS estimates on a uniform recall period of 30 days for all

items of expenditure. The experiments with an alternative 7-day recall period in the

annual, "thin sample" rounds for 1994-95, 1995-96 and 1996-97 in respect of food, paan,

tobacco and intoxicants had yielded estimates of per capita expenditure on these items

that were substantially higher than those obtained on the 30-day recall. Deaton has

rightly cautioned about the possibility of the 'telescoping' factor being present in the

estimates on the 7-day recall, so that "higher" is not necessarily better. Nevertheless one

must accept that a long recall period of 30-days in respect of these frequently purchased

items could have resulted in depressing the reported per capita consumption below some

"true" value. So that, a part of the difference that is observed between the NAS and the

NSS-based estimates (when the comparison is with the estimates based on the 30-day

reference period) could be, and, perhaps is real.

The above noted factor would also be present in respect of the difference between

the two estimates for the consumption of vegetables and fruits. But the detailed NAD-

SDRD study shows that, in respect of vegetables, adjusted for differences in unit values

(in fact, even without such an adjustment), the NSS-based estimates are higher by

Rs.4362 crores. So that the overall excess of NAS-estimates over the NSS-based

estimates for vegetables and fruits as a group (of 38,000 crores) is primarily from the

divergent estimates for consumption of fruits. These differences are sizeable in respect of

Banana, Coconut and Mango among fruits where the data base for estimates of

production are relatively firmer and groundnuts3. As noted earlier in the context of the

NAS estimates of consumption of oil seeds, the higher NAS estimate of consumption of

groundnuts (higher by Rs.2647 crores after adjustment for prices) would seem to reflect

the same assumption: that all of the groundnuts retained by producer households are

consumed, as groundnuts, by the producer households!

Both oil seeds and groundnuts reflect a deeper problem: the implicit assumption

of no changes in stocks with producer households. This, in turn, is part of a larger

problem with the NAS-estimates: virtual absence of information on privately held stocks

- of any commodity - outside of the organised sector. Even in respect of the organised

sector, outside of the Annual Survey of Industries, NAS estimates are based on sample

surveys with their own sampling and non-sampling errors.

To revert to fruits and vegetables, by far the largest contributor to the NAS-NSS

difference is a catch-all category of "other fruits", where the NAS estimates exceed the

NSS estimates by Rs.29,482 crores. In other words, this one single item accounts for a

little over 32 percent of the difference between the NAS and the NSS-based estimates for

food, beverages and tobacco as a group.

As the NAD-SDRD study itself notes, "while the cereal and pulses consumption

is estimated to be Rs.78 thousand and Rs.12 thousand crores respectively in the NAS,

that for "fruits" alone is Rs.48 thousand crore. Moreover, the estimated consumption of

fruits alone is found to exceed the consumption of vegetables and 'meat, fish and eggs'

taken together". At the heart of the problem would appear to be the underlying data base

for output and prices of fruit. For the NAS, as the NAD-SDRD study notes, "the

National Horticulture Board (NHB) is the main source for the production and price data

for the fruits not covering in area and production statistics of DES. The NHB compiles

data on area, production and productivity through the State Horticulture Board (SHB). It

has however been noticed that there is a sizeable divergence between the figures the

SHBs supply to DES and those to NHB". Further comment on the sizeable divergence

between the two estimates of consumption of fruits is superfluous.

As regards the estimates of consumption of tobacco and products, Prof. Minhas

had commented over a decade ago that household surveys are poor instruments for

collection of data on consumption of products that have a measure of social stigma or

taboo. Consequently it may be readily accepted that 'true' levels of consumption of these

products are significantly higher than what is reported in the NSS surveys. But without a

3 In respect of banana (as also cashew nut) the NAS estimates assume "that none of the two fruits are used in other industries as intermediate consumption". And in respect of mango only 30 percent of the market

clear understanding of how and why the NAS estimate of consumption of this items for

1993-94 jumps from Rs.8534 cores in NAS 1999 to 12,809 crores - i.e. by nearly 50

percent - in NAS 2000, the extent of divergence between the two estimates (Rs.6432

crores) is less readily acceptable.

Curiously, the agreement between the two estimates is rather close in respect of

alcoholic beverages and intoxicants with a difference of just 5.5 percent. This is just one

clear case where perhaps both estimates are equally bad.

The final item under the broad rubric of food items, is related to the difference

between the NSS-based estimate for purchased 'cooked meals' and NAS estimate for

Hotels and Restaurants. This difference is of the order of Rs.2,377 crores or a little over

2 percent of the aggregate difference for "all food". In part at least this difference is

attributable to the fact that the NAS estimate includes the accommodation charges (about

9 percent of the receipts as per the Enterprise Survey) as also receipts from sale of food

and beverages other than cooked meals.

Before we turn to look at the difference between the two estimates in respect of

non-food items, it would be useful to summarise the principal results in respect of "all

food" which accounted for 54 percent of the aggregate excess of NAS estimate over the

NSS based estimate - excluding the notional elements. This is done in the form of

"bullets".

• Adjustment for differences in unit values eliminates over six percent of the

difference in respect of "all food".

• Divergence in respect of items covered by the (unrealistic) assumption of zero

or low (5 percent) use for intermediate consumption, including, maida, fish,

banana and cashewnut, aggregates to Rs. 26,473 crores i.e. 29 percent of the

difference between the two sets of estimates for "all food".

suplies are assumed to be used as intermediate consumption.

• In respect of 2 items, Groundnuts and other oil seeds the divergence between

the two estimates (Rs.6122 crores or about 7 percent of the divergence for all

food) is the result of the assumption underlying the NAS estimates that the

entire amount retained by producer households is consumed as groundnuts

and oil seeds by them. Essentially a reflection of no information on privately

held stocks.

• In respect of consumption of vegetables, the NSS estimates are higher.

• Divergence of over Rs.29 thousand crores (or 32 percent of the difference for

all foods) in respect of other fruits (which is larger in absolute size than the

total NAS stimate of consumption of 'meat, eggs and fish') seems to arise

largely from the weak data base underlying the production and prices going

into the NAS estimates.

• Directionally, the higher NAS estimate of consumption of tobacco and

products can be readily accepted but the acceptability of the order of the

difference (Rs.6432 crores) is coloured by the unexplained jump (of Rs.4275

crores) in the NAS estimates as between NAS 1999 and NAS 2000.

• A part of the explanation for the higher NAS estimate for "all food" may be

traced to the possible understatement of consumer expenditure in the NSS

Consumer Expenditure Surveys due to recall lapse with the 30 day reference

period.

• Overall, depending on the allowance to be made for such "recall lapse" related

understatement in the NSS Surveys, upto 75 percent of the divergence

between the two estimates in respect of all food items taken together, may be

traced to either divergence of unit values or poor or infirm data base or

patently untenable assumptions of zero or very low use for intermediate

consumption.

In respect of expenditure on "non-food" items as a group, adjustment for

"notional" element in the NAS estimates - imputed rent, banking services and insurance

services - reduces the excess of NAS estimates over the NSS-based estimates from 128

thousand crores to 79 thousand crores. Four item groups - clothing and footwear,

furniture, furnishing, appliances and services, Transport equipment and operational cost,

and Transport Services - account for over 89 percent of the excess of NAS estimates for

non-food items as a group relative to the corresponding NSS-based estimates.

Two other items, Medical care and health services (Rs. 1322 crores) and

Education (Rs.5508 crores) account for a further 9 percent of the overall difference

between the two estimates of private consumption expenditure on all non-food items

taken together. In one case, Medical care and health services (where the NAS estimates

are directly carried over from NSS) the excess of Rs.1322 crores is seen to be an error

arising from double-counting of employees' contributions to CGHS. In respect of

education, almost all of the difference between the two estimates reflect the activities of

non-profit institutions serving the households.

As for expenses on fuel and power, taken as a group, NSS estimates exceed the

NAS estimates. This is also true of individual components except charcoal and Gobar

Gas.

Before we consider the "big ticket" items, it is useful to recall the general method

of deriving the NAS estimate of private consumption of manufactured goods. As the

NAD-SDRD study notes, "the commodity wise value of consumption of manufactured

goods is derived from the estimate of value of production, by applying various ratios and

norms respectively: (i) percentage share of consumables, (ii) gross distributive margin,

(iii)percentage shares used for fixed capital formation and inter-industry consumption and

Government Consumption". The only firm and current data base relates to Government

Consumption. For registered manufacturing ASI provides a firm data base but, since the

detailed results of ASI come with a fair measure of time lag, the commodity-wise shares

of consumable items in the total output (of product and by product), use of ratios from an

earlier ASI become ineivtable. This does not affect the comparison for 1993-94 as the

ASI-based NAS estimates use the detailed results from 1993-94 Annual Survey of

Industries. For the unregistered manufacturing, product and by-product ratios to value

added have been worked out from the Enterprise Survey in Unorganised Manufacturing,

1994-944. The percentage shares of capital formation are based on the norms worked out

on the basis of the results of the All-India Debt and Investment Survey, 1981-82.

There are, thus, a number of rates and ratios at work including one set dating back

to 1981-82!

In respect of clothing and footwear, the NSS estimates on the 365 day reference

period (canvassed and reported for the 50th Round Survey) are significantly higher (by

close to Rs.8000 crores in respect of clothing and about Rs.650 crores for footwear) than

the estimates based on the 30-day recall period which are the ones used in this exercise at

cross validation. Coincidentally, the difference between NSS (30-day) estimates

(Rs.18,203 crores) and the NAS 1999 estimate of PFCE on clothing (Rs.26,230 crores) is

also about Rs.8000 crores. The extra difference of about Rs.4700 crores comes about

precisely because of the jump in the NAS estimates between NAS 1999 and NAS 2000,

with the latter serving as the comparator estimate.

The broad item group, Furniture, furnishing, appliance and services accounts for

close to 15 percent of the difference for "all non-food" (after adjusting for the 'notional'

elements). Of this, more than half is accounted for by the sub-group 'glassware,

tableware and utensils' and of this latter, again, more than half the difference is

contributed by the item 'other metal/household utensils". There is really no satisfactory

explanation for this large difference between the two categories in respect of these goods.

The really significant part of this story is about what is not the major contributor

to the difference between the two estimate in this broad group. In respect of the sub-

group, "Freeze, cooking, washing appliances", as a group the NSS estimates are about 20

percent lower than the NAS estimates. Even in respect of 'Refrigerators and

Airconditioners' the difference, though higher, is well below 40 percent. In respect of

purchase of "Mobike, Scooter & Cycle" (under the 'Transport Equipment and operational

costs' category) too, the NSS estimate is lower than the NAS estimate by just about 8

4 Note that the annual estimates of GVA in unorganised manufacturing are themselves obtained by moving forward bench-mark estimates for a base year by reference to some physical indicators (often based on

percent. This difference between the two estimates in respect of wooden and steel

furniture is also a shade below 11 percent. Against this backgroup claims of vast under

estimation (by a factor of four or five) of private consumption of durables in the NSS

Consumer Expenditure Survey appears to be, well, vastly exaggerated. Also, simple

comparisons of production and sales of many of these durables, which are, in the NAS

parlance, partly capital goods, with NSS estimates of household consumption, would

appear to miss this dual use aspect of these goods.

In respect of Transport equipment and operational cost, (the divergence here

accounts for 22 percent of the difference between these two estimates for "all non-food")

the single largest source of divergence relates to the consumption of petrol and diesel

(over Rs.12 thousand crores), with related repairs and repair services adding a further

Rs.4500 crores. Apart from a measure of duplication in repair costs in the NAS

estimates, this sizeable difference between the two estimates would appear to turn on the

allocation of the vehicles on the road as between the households and non-profit

institutions serving the households, and, the use of "the allowance prescribed for

computing rebate on income tax in respect of repairs and maintenance of different

vehicles", as the basis for computing the per vehicle operating cost. Both these aspects

would bear further scrutiny.

Finally transport services. Underestimation of household expenditure in respect

of Air fare and Rail fare in the NSS Survey may be conceded. For the other modes of

mechanised road transport, the gross passenger earnings are estimated as the product of

an estimated average 'earnings per vehicle' and the estimates of total number of vehicles

available from the Ministry of Surface Transport (MoST). As the study itself recognises,

the key issues are: (i) whether the MOST estimates represent the actual number in

operation; (ii) the validity of estimates of per vehicle earnings used at present for the

NAS estimate; and, (iv) the validity of the assumed ratios of private consumption of these

services used for deriv ing the NAS estimates.

ASI) and current price estimates obtained by adjusting for price-inflation.

Overall, in the non-food category, a fair measure of underestimation in the NSS-

estimates of consumption of clothing, footwear, durables and some items of personal

goods - jewellery for example - must be conceded. However, apart from duplication

(repair services and medical services) and the allocation of expenses incurred by non-

profit institutions serving households (education and operational costs of transport

equipment) and possible overvaluation (domestic services), the validity of some of the

key rates and ratios underlying the NAS estimates (proportion of vehicles in actual

operation, average earnings per vehicle, and the assumed ratios of private consumption)

remains an open question. Taking the estimates of consumption of non-food items

considered together, items affected by us yet unresolved doubts about the NAS estimates

contribute the lion's share of the difference between the two sets of estimates.

We have presented the results of this important exercise at cross validation in

considerable detail not to deny the presence of a fair measure of under estimation in the

NSS estimates - in respect of both food and non-food items - but to reinforce Prof.

Minhas conclusion over two decades against "mindless tinkering" with the NSS size-

distribution by reference to simple (simplistic?) comparisons between the NAS estimates

of PFCE and the NSS-based estimates. If, in the numerous cases discussed above, the

weaknesses of the underlying data base and the fragility of the rates and ratios used,

strongly militate against taking the differences between two estimates at face value, there

is even less basis for using this "difference" to adjust the NSS size distribution.

A final point. Let us revert back to the issue of level versus change considered in

the last section. Notwithstanding all of the above, some people would continue to

"adjust" the NSS-based poverty estimates not merely by a scalar adjustment, but by

modifying, at the level of individual surveyed households, their reported consumption of

individual item of consumption, by a number of uniform scalars. This, of course, goes

beyond "tinkering", it is a conscious attempt at altering the size-distribution. It is still a

free country.

It is important to stress that, even these exercises, mindless as they are, will still

leave the picture of "change" unaltered -unless there is evidence of growing divergence

between the two sets of estimates. Despite loud claims to this effect, the evidence largely

consists of inappropriate comparisons: with the 1980-81 series for the earlier years and

the 1993-94 series for the recent years. (See Sen, 2000) There is a related issue here:

while the NSS estimates relate to the survey years and the results cannot be revised,

periodic and continuing revisions of NAS estimates for the same year are far from

uncommon.

Focusing on changes in the poverty situation, the NSS Consumer Expenditure

Surveys remain the only viable data base. It is in this context that issues of inter-se

comparability of the Surveys across years assume great importance. These are examined

in the next (and final) section.

Table II.1: Alternative NAS Estimates of Private Final Consumption Expenditure, By Broad Items for 1993-94 at Current Prices

PFCE - Estimates

(Rs. Crore) Item of Expenditure NAS

1998 NAS 1999

NAS 2000

NAS 1999 MINUS NAS 1998

NAS 2000 MINUS NAS 1999

1. Food, Beverage & Tobacco

271,474 318,065 315,243 56,731* 13,994*

1.1 Food 246,521 298,182 290,841 51,661 8,871 1.1.1 Cereals & Bread 74,482 82,264 80,267 7,782 (-)1,997 1.1.2 Pulses 11,160 11,615 11,994 455 379 1.1.3 Sugar & Gur 21,389 21,815 20,162 426 (-)1,653 1.1.4 Oils and Oil seeds 22,342 24,144 23,204 1,802 (-)940 1.1.5 Fruits and Vegetables 30,993 62,338 62,570 31,345 232 1.1.6 Potato & Other Tubers

6,088 6,145 6,205 57 60

1.1.7 Milk & Milk Products 45,788 47,502 46,594 1,714 (-)908 1.1.8 Meat, egg and fish 22,107 22,946 21,737 839 (-)1,209 1.1.9 Coffee, tea and tobacco

4,596 5,787 5,852 1,191 65

1.1.10 Spices 6,186 7,988 8,015 1,802 27 1.1.11 Other Foods 1,390 5,638 4,237 4,248 (-)1,401 1.2 Beverages, Pan & Intoxicants

8,144 5,929 5,951 (-)2,215 122*

1.2.1 Beverages 3,692 2,875 2,947 (-)817 72 1.2.2 Pan & other Intoxicants

4,452 3,054 3,004 (-)1,398 (-)50

1.3 Tobacco & its Products 10,968 8,534 12,809 (-)2,434 4,279 1.4 Hotels & Restaurants 5,841 5,420 6,142 (-)421 722 2. Clothing & Footwear 52,510 30,573 34,999 22,321* 4,988* 2.1 Clothing 48,359 26,230 30,937 (-)22,129 4,707 2.2 Footwear 4,151 4,343 4,062 192 (-)281 3. Gross Rent, Fuel & Power

48,421 68,880 70,484 20,845* 2,013*

3.1 Gross Rent & Water Charges

27,601 47,483 49,484 19,882 2,001

3.2 Fuel & Power 20,820 21,397 21,385 963* (-)12 3.2.1 Electricity 3,926 3,926 3,926 NIL NIL 3.2.2 LPG 1,714 1,521 1,521 (-)193 NIL 3.2.3 Kerosene oil 2,906 2,906 2,906 NIL NIL 3.2.4 Other Fuel 12,274 13,044 13,032 770 (-)12

Item of Expenditure NAS 1998

NAS 1999

NAS 2000

NAS 1999 MINUS NAS 1998

NAS 2000 MINUS NAS 1999

4. Furniture, Furnishing Appliances and Services

14,849 16,940 17,610 3,411* 1058*

4.1 Furniture, Furnishing & repair

909 1,458 1,312 549 (-)146

4.2 Refrigerator, Cooking, Washing appliances

1,689 1,530 1,559 (-)159 29

4.3 Glassware, tableware & Utensils

7,825 7,324 7,679 (-)501 355

4.4 Other goods 2,687 3,209 3,689 522 480 4.5 Services 1,739 3,419 3,371 1,680 (-)48 5. Med. Care & Health Services

10,984 19,543 19,543 8,559 NIL

6. Transport & Communication

60,940 64,376 65,993 3,678* 617

6.1 Personal trspt. Eqpt 2,391 2,284 2,294 (-)107 10 6.2 Operation of trspt. Eqpt 18,794 22,290 22,298 3,496 8 6.3 Purchase of trspt. Service

354,861 35,847 36,143 (-)14 296

6.4 Communication 3,894 3,955 4,258 61 303 7. Recreation, Edu. & Cul. Services

116,690 17,554 17,626 1,916* 110*

7.1 Eqpt, paper & stationery

5,208 6,349 6,330 1,141 (-)19

7.2 Recreation & Cul. Services

1,639 1,113 1,204 (-)526 91

7.3 Education 9,843 10,092 10,092 249 NIL 8. Misc. Goods & Serv ices

23,059 31,308 36,519 8,249 5,537*

8.1 Personal care & effect 4,926 5,758 10,897 832 5139 8.2 Personal goods n.c.c. 10,862 11,860 11,697 998 (-)163 8.3 Other Misc. services 7,271 13,690 13,925 6,419 235 Total PFCE 498,927 567,239 577,402 125,710* 28,317* *: The values marked with * relate to the sum of absolute differences in the appropriate sub-groups.

III Issues and Empirical Exercises Relating to Comparability of NSS-

Based Results Over Time

III.1 Comparability Problems Across NSS Rounds

Given that poverty line is specified in terms of per capita (total household)

consumer expenditure (pcte), poverty calculations require comparable size distributions

of pcte over time. The available size-distributions come from the National Sample

Surveys (NSS) of household consumer expenditure (HCE).

We focus on issues of comparability between the last two quinquennial rounds of

NSS for the years (July-June) 1993-94 and 1999-2000 and the earlier four quinquennial

rounds5 which have been carried out since 1972-73.

The first five quinquennial rounds canvassed the schedules of consumer

expenditure survey (CES) and employment-unemployment survey (EUES) on an

identical set of sample households and the published results on size-distributions of

household consumer expenditure (ranked according to size of pcte-class intervals) for

these five rounds are based on uniform reference or recall period (URP) of 30 days

(preceding the date of interview) for all items of HCE.

In the 50th round for 1993-94, information on certain infrequently purchased

items, namely, clothing, footwear, durables, education and health expenditure

5 NSS has carried out HCEs virtually annually from 1950 to 1973-74 though the lengths of survey periods have differed. A decision was taken to stop the annual rounds and move to quinquennial rounds with larger sample of households and combining HCE enquiry with that on employment and unemployment (EUE). After three quinquennial surveys, annual rounds of HCEs have been resumed in 1986-87. Six quinquennial surveys - two each in the last three decades - have been carried out. Their respective rounds and survey periods are: i. 27th Round for November-October 1972-73 ii. 32nd Round for July-June 1977-78 iii. 38th Round for January-December 1983 iv. 43rd Round for July-June 1987-88 v. 50th Round for July-June 1993-94 vi. 55th Round for July-June 1999-2000

(institutional) was collected for 30-day as well as 365-day recall periods from each

surveyed household. The published size distributions of pcte for this round are based on

URP of 30-days for all items of HCE.

In the 55th round for 1999-00, four changes were made compared to the previous

quinquennial rounds. One, enquiries on CES and EUES were canvassed on independent

sets of sample households on considerations of respondent fatigue. Two, while the CES

enquiry, as in the previous quinquennial rounds, canvassed a detailed schedule (DS) of

items of consumer expenditure to minimise recall lapse, the EUES enquiry canvassed a

considerably abridged schedule (AS) of items because per capita consumer expenditure

was merely a classificatory variable for tabulation of employment characteristics and not

the main subject of enquiry in EUES. Three, in respect of expenditures on infrequently

purchased items namely, durables, clothing, footwear, education and institutional health

expenditure, a single 365-day reference period was used in the CES as well as the EUS

enquiries. Four, in the CES, information on frequently purchased items, namely, food,

paan, tobacco and intoxicants, information was collected for two alternative reference

periods of 7-days and 30-days in blocks located side by side. In the EUES, the

information on these items was canvassed on a single reference period of 30-days. In

respect of all the remaining items of expenditure, a 30-day reference period was used in

both CES and EUES.

The published results of the 55th round for CES and EUES are based on a mixed

sreference period (MRP) of a 365-day recall for durables, clothing, footwear, education

and institutional health expenditure and a 30-day recall for all the remaining items.

Two-fold comparability problems arise between the 50th and the 55th rounds.

a. Published results of the 50th round CES are based on a uniform 30-day

recall period (URP) for all items of consumer expenditure whereas the

corresponding results of the 55th round are based on 365 day recall for

durables, clothing, footwear, education and institutional health expenditure

and 30 day recall for all the remaining items. We call this as mixed

reference period (MRP). Since the 50th round for 1993-94 canvassed the

information on clothing, footwear, durables, education and institutional

health expenditure on both 30-day and 365-day recall basis, we recalculate

MRP-based size distribution of pcte from the unit level records of the 50th

round and examine the consequences of URP and MRP on size

distributions of pcte and on headcount ratio for the 50th round (Section

III.3 below).

b. For food, paan, tobacco and intoxicants, the 55th round EUES enquiry

collected information on a single, 30-day reference period, but in using the

abridged schedule, generates results that are not strictly comparable to the

50th round based on a detailed schedule of items as far as these items are

concerned.

The CES enquiry in the 55th round used the detailed schedule of items that

was comparable to the 50th round but collected information on these items

for two recall periods of 7-days and 30-days in blocks positioned side-by-

side in the detailed schedule. For our immediate purpose of

comparability, the apprehension relates to the field level practice with

regard to two alternative possibilities depending on the order in which

information was sought and their mutually independent reporting that the

investigators were required to ensure.

When the respondents are asked to furnish information on an identical set of items

for two alternative recall periods in the blocks positioned side-by-side, two extreme

possibilities appear possible: (1) respondent(s) may have recalled for 7-days and reported

for 30-days making multiplicatory adjustment or (2) respondent(s) may have recalled for

30-days and reported for 7-days by division. Instructions to the field staff did not

explicitly mention the sequence in which the information was to be elicited from the

respondents. A letter was indeed sent by the Sampling Design and Survey Research

Division of NSS Organisation on August 19, 1999 asking the investigators to elicit

information fist for the last 30-days for all the items and then seek the same on the last 7-

days.

We have noted the two extreme possibilities because they lie at the root of the

apprehension with respect to the comparability of the 55th round data with the previous

rounds. We wish to emphasize, however, that independent reporting of the same item for

two alternative recall periods might have taken place because the information was

collected for a detailed list of items (to help recall) first for one recall period for all the

items in the block and then for another recall period for the same items. There would,

therefore, be an interval in time for eliciting information on the same item at detailed

level for two alternative reference periods - even if they are to be recorded in blocks

positioned side by side. If the reporting for the two recall periods has been independent

of each other, the problem of comparability with the previous NSS rounds does not arise

in respect of these items.

Turning to the extreme possibilities mentioned above, a sizeable proportion of the

respondents had reported their consumption on the 30-day reference period by a

multiplicatory adjustment from a 7-day recall, it would make the results from the 55th

round CES enquiry non-comparable to the results from all the previous quinquennial

rounds for the items under considerations. If, on the other hand, the responses were

indeed based on a 30-day recall, then, the estimated head count ratios based on the CES

enquiry with a 30-day reference period for food, paan, tobacco and intoxicants would be

comparable to our (recalculated MRP-based) estimates for the 50th Round.

Now, the head count ratios estimated from the size-distribution of consumer

expenditure from the 55th Round CES enquiry (based on the Key Results published

earlier) with a 7-day reference period for food, paan, tobacco and intoxicants had been

found to be uniformly lower than those based on the size-distribution with a 30-day

reference period for the specified items. (See Table III.1 reproduced from Tendulkar

(2001)), lines 1 to 6). So that, if indeed the recorded responses on the 30-day reference

period had been influenced by the recall on the 7-day reference period, directionally, this

would tend to impart a downward bias to the resultant estimates of head count ratio in

comparison with the earlier rounds. This has given rise to the apprehension that the

estimates of headcount ratio (HCR) based on the published results of the 55th CES

enquiry with a 30-day reference period for food, paan, tobacco and intoxicants would

understate the demand head count ratio and hence can at best provide only a possible

lower bound on HCRs for 1999-2000.

On the other hand, the 55th round EUES enquiry is comparable in terms of recall

periods of the earlier quinquennial rounds (except in respect of clothing, durables etc.

canvassed with a 365-day reference period) but is based on the abridged schedule rather

than detailed schedule. The household reporting of consumer expenditure from the

abridgement of the schedule is known to be affected by a greater degree of recall lapse

than a detailed schedule and hence would tend to understate the total consumer

expenditure in comparison with that based on a detailed schedule. This downward bias

may be expected to shift the size distribution of per capita (monthly household) total

consumer expenditure (pcte) based on EUES to the left of that based on CES for the 55th

round.

Published results of the 55th round for EUES and CES indicate that the cumulative

distribution function of pcte based EUES lies uniformly above that based on CES. This is

so at the all-India level (See charts 1 and 2) as well for rural and urban populations of

most of the 15 major states in India (not reported here). This suggests that headcount

ratio based on EUES size distribution would be higher than that based CES and hence,

would at best only provide an upper bound to the comparable headcount ratio for the 55th

round. (See Table III.1 and compare lines 7 and 8).

Is there some way to assess whether 7-day recall for food, paan, tobacco and

intoxicants has been mostly recorded in practice and may have affected the 30-day recall

making it non-comparable to the earlier rounds or 30-day recall may have been mostly

recorded and affecting 7-day recall-based observations? Making certain assumptions

(necessitated by the use of published data), an exercise (Tendulkar (2001)) reached a

tentative conclusion that the estimated CES-based headcount ratios for the 55th round

were more likely to be nearer to those based on the 30-day recall than those based on the

7-day recall and hence were likely to be comparable to the MRP-based recalculated

headcount ratios for the 50th round.

Tendulkar (2001) made the following assumptions which were then unverifiable

from the published data but can now be verified from the unit-level data for EUES

released by NSSO.

1. Mean pcte within each class-interval for the size-distribution of consumer

expenditure given in EUES published results was the same as that

emerging from CES for the 55th round. Being merely a classificatory

variable for results relating to employment-unemployment, mean pcte was

not reported in the published results for EUES;

2. Using assumption (1) he derived the average pcte for the entire rural and

urban population separately and comparing them with those from CES he

found that the CES-based estimate was higher by 9 percent (rural) and 11

percent (urban). This was to ascertain the magnitude of expected under-

statement from the abridgment of the schedule;

3. Making a uniform shift of EUES-based size-distribution to the right by the

degree of under-statement derived in (2), he estimated the adjusted

headcount ratios reported in line 9 of Table III.1.

The hypothesis was that given that EUES-based headcount ratio (HCR) being

solely based on 30-day recall would be unaffected by the CES-based 7-day-30-day recall

problem (except for the understatement arising from the abridged schedule). If the

adjusted HCR on assumptions (1) to (3) was nearer to the CES-based HCR, then the

CES-based data on food, paan, tobacco and intoxicants was nearer to the 30-day recall

and can be assumed to be not affected by the 7-day recall period. Comparing lines 7 and

9 of Table III.1, he concluded that the hypothesis might be taken as tentatively confirmed

and that CES-based HCR would be comparable to the MRP-based HCR for the 50th

round adjusted for 365 day recall for clothing, footwear, durables, education and medical

(institutional) expenditure. In a subsequent section (III.4 below) we verify assumptions

(1) to (3) on the basis of the unit-level records and present additional evidence to confirm

the comparability of HCRs based on the 50th and the 55th rounds of NSS (Section III.5).

III.2 Comparability of the 50th and the 55th rounds of NSS

Analysis based on the Unit-Level Records

Following the issues relating to comparability posed and partially analysed on the

basis of the Key Results (NSSO (2000)) published earlier in the last section, we now turn

to the analysis based on the unit-level records for the CES in the 50th round for 1993-94

and the unit-level records on consumer expenditure from EUES for the 55th round and

supplement them with the published results of CES for the 55th round. In particular, we

examine the following:

1. The effects on the size-distribution of pcte of the 365-day recall for

clothing, footwear, durables, education and health (institutional)

expenditure and compare them with that based on the (published) 30-day

recall for these items for the 50th round (Section III.3).

2. The effects on the size-distribution of pcte of the abridgment of the

consumer expenditure by comparing the EUES-based size-distribution for

the 55th round with the CES-based size-distribution, in the process

verifying the validity or otherwise of the assumptions made by Tendulkar

(2001). (Section III.4).

3. A comparison of per capita consumer expenditure (averaged for the entire

rural population) by commodity groups between CES and EUES for the

55th round to throw additional light on the 7-day and 30-day recall

controversy. (Section III.5).

It may be recalled from Section III.1 that comparisons (2) and (3) above are based

on independent samples of households from the same universe of households. This was

the departure for the first time from all the earlier quinquennial rounds where CES and

EUES enquiries had been canvassed on identical sets of sample households.

III.3 Effects of 30-day and 365-day recall: the 50th Round

As mentioned in section III.1, the information in CES for the 50th round (1993-94)

on clothing, footwear, durables, education and health (institutional) was collected for two

alternative reference periods of 30-days and 365-days from each sample household.

These are infrequently purchased items on which independent information can be

plausibly elicited from the same households without one affecting the other. For all the

remaining items, a uniform 30-day recall was used in the 50th round. We can thus

compute two alternative size distribution, one based on a uniform (30-day) reference

period (URP) and another based on mixed (365-day for above mentioned items and 30-

day for remaining) reference period (MRP). This is important for comparability in view

of the shift to MRP in the 55th round.

Table III.1 (rural) and III.2 (urban) present the size distribution of total household

consumer expenditure according to 5% fractile groups when households are ranked

according to the size of per capita total (consumer) expenditure (pcte).

It may be noted that a shift from 30-day recall to 365-day recall in respect of

clothing, footwear, durables, education and health (institutional) expenditure leads to

higher mean pcte for the fractile groups in the bottom 65 percent of the rural and bottom

70 percent of the urban population. In other words, for these sections of the population

mean per capita (monthly) expenditure on the above-mentioned items was higher on the

basis of recall for last 365-days than the same based on the 30-days recall preceding the

interview. In contrast, for the top 35 percent of the rural and 30 percent of the urban

population, mean (monthly) per capita household expenditure on these items was lower

in the last 365-days than in the last 30-days. (This shows the conjecture of Tendulkar

(2001) in this regard to be wrong). In consequence, the overall mean pcte turns out to

be lower by 2.6 percent (rural) and 1.8 percent (urban). The corresponding Lorenz

curves (LC) presented in Chart 3 (rural) and 4 (urban) show that the LC based on 365-day

recall (mixed reference period or MRP) lies uniformly inside that based on 30-day

uniform recall period (URP). Consequently, the summary measure of relative inequality

based on the LC, namely, Gini coefficient is distinctly lower at 0.2379 (rural) and 0.3189

(urban) based on MRP than 0.2678 (rural) and 0.3409 (urban) based on URP.

Since the reported pcte for the bottom fractile groups is higher under MRP than

that under URP, the headcount ratio (presented subsequently in section III.6) based on

MRP is expected to be lower than that based on URP.

III.4 Abridgment of Consumer Expenditure Schedule: 55th Round,

Effects on Size-Distribution

We have noted in Section III.1 that an abridgment of the list of items for eliciting

information on consumer expenditure used in EUES was expected to result in a lower

reported pcte due to recall lapse than that under a detailed schedule of items used to help

recall in CES. How does this affect the size-distribution of pcte? For this purpose, we

compare three size distributions:

1. CES-based size distribution published by NSSO by pcte class intervals

based on a detailed schedule of items;

2. EUES-based size distribution by the same pcte intervals as in the

published report of CES (EUES-1 for short);

3. EUES-based size distribution of pcte according to fractile-groups of the

population formed according reported pcte in EUES (EUES-2 for short).

NSSO has been choosing the size classes of pcte in CES according to 12 fractile

groups: the top and the bottom decile divided into two 5% fractile groups each and the

remaining middle 80 percent of the population divided into 8 deciles. Size distributions

(1) and (3) above are according to this specification of the sizes of the fractile groups.

Since the end-points at either end of pcte class-intervals are rounded off-to the nearest

integer the sizes of fractile groups in CES-based published size-distribution differ slightly

from the ones mentioned above. The differences in the size distributions arise because of

the differences in the numerical value of the ranking criteria. In the CES size distribution

(1), households are ranked according to size of pcte reported from the detailed schedule

of consumer expenditure enquiry whereas in (2) and (3) the ranking criterion is size of

pcte reported from the abridged schedule of items in EUES. Tables III.4 (rural) and III.5

(urban) present the size distributions (1) to (3) above. CES-based size distribution is

taken directly from the published report whereas EUES-1 and EUES-2 have been worked

out from the unit level records.

A comparison of CES-based size distribution with EUES-1 brings out a shift of

the entire size distribution under EUES to the left of that under CES. This shift reflects

the expected understatement of pcte due to the effect of recall lapse resulting from the

abridgment of the schedule (discussed in III.1). Mean pcte in all the bounded intervals

does not differ much between CES and EUES-1. The difference appears only for the top

most open-ended pcte interval.

At the aggregate level, mean pcte under CES turns out to be 9.8 percent (rural)

and 12.2 percent (urban) higher than that under EUES. This is somewhat different from

9 percent (rural) and 11 percent (urban) reported in Tendulkar (2001) using the same

mean pcte in the different pcte intervals as reported by CES.

A comparison of shares in total expenditure from Tables III.4 and III.5 between

CES and EUES-2 shows that differences are only marginal and appear only at the top and

the botton 5% fractile groups. Consequently the plotted Lorenz curves do not show any

visible differences and hence are not presented. The differences in the two size

distributions come out sharper from the cumulative relative size distributions in Charts 1

(rural) and 2 (urban) presented earlier in connection with our discussion in Section III.1.

Gini coefficients based on identical sizes of fractile groups under CES and EUES-2 show

that they are lower at 0.2533 (rural) and 0.3285 (urban) under EUES-2 than 0.2604

(rural) and 0.3420 (urban) under CES.

Finally, we examine the assumptions of a uniform shift in the size distribution in

Tendulkar (2001) in Table III.6 which presents percentage understatement of the

abridged scheduled-based pcte in EUES with respect to the detailed schedule based pcte

used in CES. These are given for the identical sizes of the frac tile groups of population

for (a) the mean pcte of the fractile group and (b) the upper terminal of pcte for each

fractile group.

It may be noted from Table III.6 that for the bottom half of the rural as well as the

urban population, the percentage short fall is somewhat lower than the average shortfall

for the entire population both for the average pcte as well as upper terminal value of pcte.

In other words, the uniform adjustment on the basis of the average pcte carried out in

Tendulkar (2001) on the basis of the information then available is clearly not borne out

though the deviations are not very large. Should we make a more accurate adjustment

now available from the unit level records and presented in Table III.6? It turns out, as we

argue in the next section, that no adjusted HCR from EUES is needed. However, if the

adjustment at the observed lower than average rate were carried out, the estimated

adjusted HCRs reported in line 9 of Table III.1 would come pretty close to the ones

reported in line 7 for the CES and hence the hypothesis indicated in Section III.1 would

be valid with greater force, namely, CES for the 55th round has, in fact, approximated

well pcte based on 30-day recall for food, paan, tobacco and intoxicants and hence, the

resultant size distribution is comparable to the corresponding (MRP-based) size

distribution for the 50th round.

III.5 A Comparison Between CES and EUES by Commodity Groups for the

Entire Rural Population: 55th Round

Introspection as well as a priori reasoning suggest that the expected recall lapse

involved in the abridgment of 15-page long detailed schedule of items canvassed in CES

to a one-page long single block in EUES in the 55th round for 1999-2000 would not affect

all items of expenditure uniformly. This requires an examination of the commodity

group wise comparison between CES and EUES ideally for each fractile group of

identical size for the rural and urban population at least at the all-India level to begin

with. We had been grappling with EUES-based consumer expenditure for the past over

one month with several rounds of sittings with NSSO staff who have been very patient

and cooperative. With the programming resources and the computer speeds at our

disposal we have so far managed to extract the aggregate per capita expenditure (pce) on

20-groups of commodities and services distinguished in the published report of CES for

the entire rural population only. This is presented in Table III.7. We are trying to extract

a similar table for fractile groups of identical size. However limited, what emerges from

an examination of Table III.7 is very striking and helps us towards resolving the 7-day-

30-day controversy for the rural population. It may be mentioned that the rural poor

constitute nearly 80 percent of the total (rural plus urban) poor persons. The following

findings are important.

One, out of 30 groups of household consumer expendiutre distinguished in Table

III.7, EUES-based pce estimates are lower, as expected, than those from CES for as many

as 17 groups.

Two, out of the total discrepancy of Rs.43.79, the maximum negative discrepancy

of Rs.16.33 (or a little over 37 percent) is accounted for by a heterogenous category of

"other food". The second highest discrepancy of Rs.11.82 (or 27 percent) goes to another

heterogenous category of "other consumer goods and services". The third highest

discrepancy of Rs.5.11 (or nearly 12 percent) is reported for "milk and milk products".

These three item groups where recall lapse could reasonably be expected to be very

significant because of their within-group diversity of composition and where reporting

would be sensitive to the number of detailed items canvassed to help recall, thus explain

over three-fourths of the total osberved discrepancy between CES and EUES. The

downward bias in all these items is consistent with prior expectation.

Thirdly, for as many as seven out of nine items groups under food, paan, tobacco

and intoxicants accounting for nearly 70 percent of the total expenditure on these items

reported in CES using the detailed schedule, the concordance between the CES-based and

EUES-based estimates of pces is amazingly close.

Recall

a. that these estimates emerge from two independent samples of households

drawn in the 55th round for CES and EUES;

b. that EUES abridged schedule used a single 30-day recall period for food,

paan, tobacco and intoxicants and was not affected by 7-day-30-day

problem;

c. that CES detailed schedule has been embroiled in a controversy regarding

the canvassing of 7-day and 30-day recalls in blocks positioned by side

and in consequence whether the published results of CES for 30-day recall

are comparable to the earlier rounds.

The close correspondence between the two sets of estimates in Table III.7 and

commented above leads us to infer, in the light of (a) and (b), that CES has captured the

household consumer expenditure on food, paan, tobacco and intoxicants on a 30-day

recall and are, therefore, comparable to the same expenditure category in the earlier

quinquennial rounds. The inference needs to be confirmed for the urban population as

well as at the disaggregated fractile groups by undertaking the commodity-group level

comparisons. This exercise is currently on and we hope to report the results later.

We would like to add a post-script that fractile-group and commodity group level

comparisons between CES and EUES have since been completed and the inference stated

above is confirmed for the rural population.

III.6 Comparable Headcount Ratios Overtime

The foregoing discussion of the problems of comparability of size distributions of

monthly per capita total (household consumer) expenditure (pcte) and our empirical

analysis based partly on published results and partly on the unit level records of the 50th

and the 55th rounds of NSS led us to the following conclusions:

1. The published size distributions of the first five quinquennial rounds

including the 50th for 1993-94 are based on a uniform (30-day) reference

period (URP) and headcount ratios calculated from them are comparable;

2. The published size distributions of the 50th round for 1993-94 and the 55th

round for 1999-2000 are not directly comparable because of differences in

the recall periods, namely, URP in the 50th round and a mixed reference

period (MRP) in the 55th round;

3. The size distribution of the 50th round can be recast for MRP and we have

recalculated it with MRP (Section III.3) to make it directly comparable to

the 55th round;

4. With reference to the 7-day-30-day controversy regarding CES in the 55th

round, evidence presented in Section III.5 suggests that the size

distribution of CES is comparable to the MRP-based size distribution.

These conclusions enables us to calculate comparable headcount ratios (HCR)

which are presented in Table III.8 for three time-points: 1983, 1993-94 and 1999-2000.

The choice of years is governed by the following considerations. The idea is to

monitor descriptively the progress in poverty reduction over the last two decades and in

the process also bring out the differences in level comparability of headcount ratios

(HCR) arising from uniform and mixed reference periods. We could have chosen the

43rd round for the year 1987-88 to compare with 1983 to represent the decade of the

1980s. However, poverty, in particular rural poverty, is known to be affected by

abnormal agricultural harvests and 1987-88 was a meteorological drought year and hence

was excluded.

Table III.8 provides two estimates for 1993-94, one based on uniform reference

period (URP) and another based on mixed reference periods (MRP). The estimate based

on URP is comparable to 1983 whereas that based on MRP is comparable to 1999-2000.

Two alternative estimates of HCR are presented for each year and for each

segment (rural/urban/total) of the population: one based on all-India size distribution and

the all-India (official) poverty line and the other is the aggregated weighted HCR for 15

major states (See notes 3 and 4 to Table III.8 for details). The estimate for the total (rural

plus urban) population is a weighted average of the corresponding rural and urban HCRs.

Notice to start with that, as indicated by our results in section III.3 a switch from

URP to MRP lowers HCR by nearly two percentage points. While the levels of HCR

cannot be compared over three time points the changes in HCR can be in terms of

average percentage points over the 10.5 years between 1983 and 1993-94 (URP) and over

6 years between 1993-94 (MRP) and 1999-2000. The numerical magnitudes differ

whether one takes a direct all-India estimate or a weight average HCR over 15 major

states. But both of them suggest that

a. using the correct comparisons the rural poverty reduction was greater than

its urban counterpart in both the periods;

b. the percentage point reduction of over one point per annum in the 1990s

was higher than the earlier period when it hovered between 0.7 and 0.9

points;

c. the published results based for 1993-94 on URP and for 1999-2000 (MRP)

would overstate the degree of reduction.

With reference to the debate whether poverty has declined in the 1990s, our

conclusion, based on the empirical results emerging from the analysis of unit level

records and published data is that rural as well as urban poverty measured in terms of the

proportion of population below poverty line (headcount ratio) has unambiguously

declined in the 1990s and the magnitude of reduction on the average was higher than in

the earlier 10½ years prior to 1993-94.

Table III.1 Alternative Estimates of Headcount Ratios for 1999-2000

Ser. No.

Reference Period

Enquiry Semi-Round

Nature Rural Urban

(1) (2) (3) (4) (5) (6) (7) 1 30-day Consumer

Expenditure 1 Unadj. 29.33 24.55

2 30-day Consumer Expenditure

2 Unadj. 29.03 22.28

3 30-day Consumer Expenditure

Comb. Unadj. 29.13 23.43

4 7-day Consumer Expenditure

1 Unadj. 25.97 22.62

5 7-day Consumer Expenditure

2 Unadj. 25.90 20.70

6 7-day Consumer Expenditure

Comb. Unadj. 25.83 21.60

7 30-day Consumer Expenditure

Comb. Unadj. 29.13 23.52

8 30-day Employment-Unemployment

Comb. Unadj. 36.45 28.76

9 30-day Employment-Unemployment

Comb. Adj. 28.08 22.12

Notes: 1. Lines 1 to 7 are based on detailed schedule of items that was canvassed in the

consumer expenditure enquiry. Line 8 is based on the abridged schedule of items of consumer expenditure that was canvassed in the enquiry on employment and unemployment.

2. Detailed schedule of items for consumer expenditure enquiry elicits information on food, paan, tobacco and intoxicants for two alternative reference periods of 7 and 30 days in blocks that are given side by side. Abridged schedule elicits information on these items for the last 30 days.

3. For adjusted estimate in line 9, we use the size distribution based on employment-unemployment enquiry as in line 8 but combined it with mean per capita total expenditures (pcte) within each pcte class-intervals as those that are reported in the consumer expenditure enquiry. This yields overall mean pcte at Rs.444.15 (rural) compared to Rs.486.54 from consumer expenditure enquiry and Rs.770.28 (urban) compared to Rs.854.47. We uniformly shift the observed size distribution from employment-unemployment enquiry to right by the 9.46 percent (rural given by 486.54/444.15) and 11.04 percent (urban given by 854.47/770.28) for the purpose of calculating headcount ratio.

Sources: 1. NSSO (2000) for lines 1 to 6 2. NSSO (2001a) for line 7 3. NSSO (2001b) for line 8.

Chart 1: Cumulative Relative Size Distributions for the 55th Round of NSS: Rural

Series 1: Abridged Schedule in employment-unemployment Enquiry, 55th Round Series 2: Detailed Schedule in consumer expenditure Enquiry, 55th Round

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

L e s sth a n2 2 5

3 0 0 3 8 0 4 7 0 6 1 5 9 5 0

M P C E (R s )

Cum

ulat

ive

Perc

enta

ge o

f Pop

ulat

ion

S e r ie s 1S e r ie s 2

Chart 2: Cumulative Relative Size Distributions for the 55th round of NSS: Urban

Series 1: Abridged Schedule in employment-unemployment Enquiry, 55th Round

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

L e s sth a n3 0 0

4 2 5 5 7 5 7 7 5 1 1 2 0 1 9 2 5

M P C E (R s )

Cum

ulat

ive

Perc

enta

ge o

f Pop

ulat

ion

S e r ie s 1S e r ie s 2

Series 2: Detailed Schedule in consumer expenditure Enquiry, 55th Round

Table III.2: NSS 50th Round: A Comparison of Size Distribution by 5% Fractile Groups Between Uniform and Mixed Reference Periods: All India: Rural Population

All India Rural

Fractile Group

Cumulative % of

Population

Average pcte

URP

Cum % CE by 30-day

Average pcte

MRP

Cum % CE by 365-day

0-5% 5 107.0568 1.90 112.7227 2.06 5-10% 10 137.1109 4.34 143.0156 4.67 10-15% 15 153.0963 7.06 159.3483 7.57 15-20% 20 166.5078 10.01 172.9196 10.73 20-25% 25 177.76 13.17 184.027 14.08 25-30% 30 188.8394 16.52 195.3137 17.65 30-35% 35 199.6443 20.07 205.8051 21.40 35-40% 40 210.4275 23.81 216.2759 25.35 40-45% 45 222.3902 27.76 226.9396 29.49 45-50% 50 234.1682 31.92 238.813 33.84 50-55% 55 246.9086 36.30 249.7347 38.40 55-60% 60 260.3121 40.92 262.1496 43.18 60-65% 65 275.3872 45.82 275.946 48.22 65-70% 70 291.8567 51.00 291.2501 53.53 70-75% 75 311.81 56.54 308.7733 59.16 75-80% 80 336.4533 62.51 329.4956 65.17 80-85% 85 368.4131 69.06 356.125 71.67 85-90% 90 414.0056 76.41 392.2514 78.83 90-95% 95 489.056 85.10 454.0539 87.11 95-100% 100 839.2015 100.00 706.6327 100.00 0-100% 281.5203 274.0796

Note: URP: uniform (30 day) reference period for all items of consumer expenditure. MRP: mixed reference period : 365 days for clothing, footwear, education and health (institutional) and 30 days for all the remaining items CE: Aggregate Consumer Expenditure Pcte: per capita total consumer expenditure

Table III.3: NSS 50th Round: A Comparison of Size Distribution by 5% Fractile Groups Between Uniform and Mixed Reference Periods: All India: Urban Population

All India Urban Fractile Group

Cumulative % of

Population

Average pcte

URP

Cum % CE by 30-day

Average pcte

MRP

Cum % CE by 365-day

0-5% 5 133.0799 1.45 138.8078 1.54 5-10% 10 175.8905 3.37 182.1801 3.57 10-15% 15 201.9348 5.58 208.7717 5.89 15-20% 20 222.8357 8.01 230.1181 8.45 20-25% 25 242.3559 10.65 250.4214 11.23 25-30% 30 261.9733 13.51 269.9378 14.23 30-35% 35 281.1159 16.58 289.2902 17.45 35-40% 40 302.5225 19.88 309.525 20.89 40-45% 45 323.6575 23.42 330.4057 24.56 45-50% 50 346.5325 27.20 353.895 28.50 50-55% 55 370.3242 31.24 376.1983 32.68 55-60% 60 397.9061 35.58 402.1393 37.15 60-65% 65 430.2546 40.28 433.106 41.96 65-70% 70 467.1801 45.38 467.8879 47.17 70-75% 75 513.6512 50.99 511.4624 52.85 75-80% 80 569.3199 57.20 564.5064 59.13 80-85% 85 641.3186 64.20 631.2962 66.15 85-90% 90 742.1016 72.30 725.5948 74.21 90-95% 95 911.4375 82.25 887.1303 84.07 95-100% 100 1626.268 100.00 1432.519 100.00 0-100 % 458.083 449.7597 Note: URP: uniform (30 day) reference period for all items of consumer expenditure.

MRP: mixed reference period: 365 days for clothing, footwear, education and health (institutional) and 30 days for all the remaining items

CE: Aggregate Consumer Expenditure pcte: per capita total consumer expenditure

CHART - 3 All India : Rural Lorenz Curve (NSS 50th Round)

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100Cumulative % of Population

Cum

ulat

ive

% o

f CE

Cum % CE by 30-day Cum % CE by 365-day

CHART- 4 All India: Urban Lorenz Curve (NSS 50th Round)

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100Cumulative % of Population

Cum

ulat

ive

% o

f CE

Cum % CE by 30-day Cum % CE by 365-day

Table III.4: A Comparison of Size Distributions of Per Capita Total (Monthly) Expenditure (pcte), Mean pcte and Shares in Total Expenditure from Consumer Expenditure Survey (CES) and Employment-Unemployment Survey (EUES): NSS 55th Round, All India Rural Population Class-Interval (Rs. Per month)

CES EUES-1 EUES-2

Share of popn (%)

Mean pcte (Rs)

Share of expend. (%)

Share of popn (%)

Mean pcte (Rs.)

Share of Expend. (%)

Share of popn (%)

Mean pcte (Rs)

Share of expend. (%)

0-225 5.20 190.98 2.05 7.80 189.57 3.33 5.00 174.53 1.97 225-255

5.00 241.82 2.49 6.23 240.48 3.34 5.00 222.61 2.51

255-300

10.00 278.69 5.74 11.81 278.31 7.39 10.00 258.52 5.83

300-340

10.00 321.04 6.61 11.61 320.27 8.36 10.00 297.14 6.70

340-380

10.30 360.83 7.65 11.08 359.74 8.96 10.00 331.75 7.48

380-420

9.70 399.90 7.99 9.70 399.25 8.70 10.00 367.01 8.28

420-470

10.20 445.49 9.36 9.92 444.23 9.90 10.00 406.06 9.16

470-525

9.30 496.74 9.51 8.04 495.91 8.96 10.00 453.63 10.23

525-615

10.30 566.62 12.02 9.02 566.24 11.47 10.00 516.90 11.66

615-775

9.90 686.00 13.98 7.75 684.24 11.91 10.00 617.49 13.92

775-950

5.00 851.50 8.77 3.59 850.59 6.86 5.00 767.00 8.65

Above 950

5.00 1354.35

13.83 3.45 1345,39

10.82 5.00 1207.55

13.61

All 100.00

486.16 100.00

100.00

443.42 100.00

100.00

443.42 100.00

Notes: 1. CES size distribution pcte class intervals and mean pcte are the same as those

given in the published NSS report on Consumer Expenditure Survey, Report No. 457, May 2001.

2. EUES-1 is the size distribution with the same pcte class intervals as in the CES and worked out from unit level records.

3. EUES-2 is the size distribution with the same size classes of fractile groups the as CES and worked out from unit level records.

Table III.5: A Comparison of Size Distributions of Per Capita Total (Monthly) Expenditure (pcte), Mean pcte and Shares in Total Expenditure from Consumer Expenditure Survey (CES) and Employment-Unemployment Survey (EUES): NSS 55th Round, All India Urban Population Class-Interval (Rs. Per month)

CES EUES-1 EUES-2

Share of popn (%)

Mean pcte (Rs)

Share of expend. (%)

Share of popn (%)

Mean pcte (Rs.)

Share of Expend. (%)

Share of popn (%)

Mean pcte (Rs)

Share of expend. (%)

0-300 5.20 255.77 1.56 7.71 250.81 2.54 5.00 231.02 1.51 300-350

5.00 327.13 1.92 6.19 326.97 2.66 5.00 299.21 1.96

350-425

9.60 389.14 4.37 10.67 388.96 5.44 10.00 359.34 4.71

425-500

10.10 463.92 5.48 11.31 462.79 6.87 10.00 428.49 5.62

500-575

9.90 537.22 6.22 10.35 536.36 7.28 10.00 495.60 6.49

575-665

10.00 618.61 7.24 10.45 619.11 8.49 10.00 567.00 7.43

665-775

10.10 718.61 8.49 9.76 718.51 9.20 10.00 651.63 8.54

775-915

10.00 840.53 9.83 8.92 841.49 9.85 10.00 760.20 9.96

915-1120

10.00 1009.67

11.81 8.80 1011.28

11.67 10.00 913.80 11.98

1120-1500

10.10 1286.19

15.20 8.18 1283.35

13.77 10.00 1159.37

15.21

1500-1925

5.00 1692.16

9.90 4.13 1685.84

9.13 5.00 1523.89

9.99

More than 1925

5.00 3074.27

17.98 3.53 2824.04

13.08 5.00 2532.35

16.60

All 100.00

854.92 100.00

100.00

762.29 100.00

100.00

762.29 100.00

Notes: 4. CES size distribution pcte class intervals and mean pcte are the same as those

given in the published NSS report on Consumer Expenditure Survey, Report No. 457, May 2001.

5. EUES-1 is the size distribution with the same pcte class intervals as in the CES and worked out from unit level records.

6. EUES-2 is the size distribution with the same size classes of fractile groups as CES and worked out from unit level records.

Table III.6: Percentage Understatement of Per Capita Total (Monthly) Expenditure (pcte) from Employment-Unemployment Survey (EUES) Relative to pcte from Consumer Expenditure Survey (CES) for Average and Upper Terminal Values of pcte by Fractile Groups of Rural and Urban Population: NSS 55th Round for 1999-2000 Percent Fractile Group of population

Rural Population Urban Population

Average Upper Terminal

Value

Average Upper Terminal

Value 0-5 -8.61 -8.12 -9.68 -8.67 5-10 -7.94 -7.36 -8.53 -8.57 10-20 -7.24 -7.05 -7.66 -7.29 20-30 -7.44 -7.48 -7.64 -7.80 30-40 -8.06 -8.13 -7.75 -8.17 40-50 -8.22 -8.23 -8.34 -8.72 50-60 -8.85 -8.84 -9.33 -9.68 60-70 -8.68 -8.33 -9.56 -9.84 70-80 -8.77 -9.29 -9.50 -9.55 80-90 -9.99 -9.83 -9.86 -9.80 90-95 -9.92 -10.02 -9.94 -9.51 95-100 -10.20 - -17.63 - 0-100 -8.79 - -10.83 -

Note: Worked out on the basis of the published NSS report on CES and unit level records for EUES.

Table III.7: A Comparison of Monthly Per Capita (Consumer) Expenditure (pce) on Broad Items Groups Between Consumer Expenditure Survey (CES) and Employment-Unemployment Survey (EUES)

55th Round of NSS (1999-2000)

All India Rural Population

(Rs. Per month) Item Group of Consumer Expenditure

CES pce

EUES pce

CES-EUES (Rs.)

% difference

1. Cereals, gram and cereal substitutes

108.75 106.25 2.50 2.3

2. Pulses and pulse products 18.50 18.18 0.32 1.7 3. Milk and Milk products 42.56 37.45 5.11 12.0 4. Edible Oil 18.16 18.05 0.11 0.6 5. Egg, fish and meat 16.14 15.72 0.42 2.6 6. Vegetables 29.98 29.74 0.24 0.8 7. Fruits; fresh and dry 8.36 6.65 1.71 20.5 8. Other Food 46.36 30.04 16.32 35.2 Total Food 288.81 262.07 26.74 9.3 9. Paan, tobacco and intoxicants 13.97 12.11 1.86 13.3 10. Fuel and light 36.56 32.03 4.53 12.4 11. Clothing 33.28 32.67 0.61 1.8 12. Footwear 5.37 5.38 -0.01 -0.2 13. Durable goods 12.76 15.63 -2.87 -30.6 14. Education 9.37 13.90 -4.53 -48.3 15. Medical, institutional 6.66 6.32 0.34 5.1 16. Medical, non-institutional 22.94 22.42 0.52 2.3 17. Entertainment 2.02 1.02 1.00 49.5 18. Goods for personal care and effects

12.96 14.65 -1.69 -13.0

19. Conveyance 14.28 10.70 3.58 25.07 20. Sundry articles and consumer services

24.51 12.69 11.82 48.2

21. Rent 1.89 1.95 -0.6 -3.2 22. Taxes and cases 0.80 NA 486.17 442.37 43.80 9.0 Source: NSSO: Level and Pattern of Consumer Expenditure in India, 1999-2000

NSS 55th Round (July 1999-June 2000) Report No. 457 (May, 2001) for CES

: EUES based on our calculations

Table III.8: Estimates of Headcount Ratios for 1983, 1993-94 and 1999-2000 S.No. Description 1983 1993-94 1999-2000 URP URP MRP CES (1) (2) (3) (4) (5) (6) A. Rural Population A.1 All India 49.02 39.61 37.87 29.10 A.2 Average for 15 major

states 51.24 42.97 39.62 31.96

B. Urban Population B.1 All India 38.33 30.87 28.84 23.52 B.2 Average for 15 major

states 40.99 33.32 31.20 24.96

C. Total (Rural plus Urban) Population

C.1 All India 46.47 37.31 35.50 27.57 C.2 Average for 15 major

states 48.77

40.50 37.47 30.07

Notes: 1. URP: Uniform Reference period of 30-days for all-items of consumer expenditure

MRP: Mixed Reference period: 30-days for all items except clothing, footwear, durables and institutional health facilities for which 365 day reference period was used

2. Line 1 in each panel is based on all India poverty line and all India size distribution for all the states and the union territories in India covered in National Sample Surveys.

3. Line 2 in each panel is aggregated estimates for fifteen major states. For each state the estimated headcount ratio is based on state specific size distribution and all India poverty line corrected for state specific price changes so that price adjusted poverty lines differ for different states.

4. Fifteen Major States are Andhra Pradesh (AP), Assam (ASM), Bihar (BHR), Gujarat (GJT), Haryana (HRY), Karnataka (KTK), Kerala (KRL), Madhya Pradesh (MP), Maharashtra (MH), Orissa (ORS), Punjab (PNB), Rajasthan (RJN), Tamil Nadu (TN), Uttar Pradesh (UP) and West Bengal (WB).

Sources: 1. Columns (3), (4) and (6) based on published NSS Reports.

2. Column (5) is based on authors' calculations from the unit level records.

References Government of India, Central Statistical Organisation (2001): Report on Cross-Validation Study of Estimates of Private Consumption Expenditure Available from Household Survey and National Accounts, prepared by NAD of the CSO and SDRD of the NSSO, (mimeo) New Delhi. Minhas, B.S. (1988): "Validation of Large Scale Sample Survey Data: Case of NSS Estimates of Household Consumption Expenditure, Sankhya, Series B, Vol. 50, Part 3, Supplement. Minhas, B.S. and S.M. Kansal (1990): "Firmness, Fluidity and Margins of Uncertainty in the National Accounts Estimates of PCE in the 1980s, "The Journal of Income and Wealth", Volume 12, No. 1. NSSO (2000): National Sample Survey Organisation, Household Consumer Expenditure in India, 1999-2000, Key Results, NSS 55th Round (July 1999-June 2000) (December). NSSO (2001a): National Sample Survey Organisation, Level and Pattern of Consumer Expenditure in India, 1999-2000, NSS 55th Round (July 1999-June 2000), Report No. 457 (May). NSSO (2001b): National Sample Survey Organisation: Employment and Unemployment Situation in India, 1999-2000, Part I, NSS 55th Round (July 1997-June 2000), Report No. 458 (May). Sen, Abhijit (2000): "Estimates of Consumer Expenditure and its distribution: Statistical Priorities after NSS 55th Round", Economic and Political Weekly, December 16-22. Sundaram, K. and S.D. Tendulkar (1993): "Poverty in Asia and the Pacific: Conceptual Issues and National Approaches to Measurement", Economic Bulletin for Asia and the Pacific, Vol. XLIV, No. 2, December. Sundaram, K. and Suresh D. Tendulkar (2001): "NAS-NSS Estimates of Private Consumption for Poverty Estimation: A Disaggregated Comparison for 1993-94", Economic and Political Weekly, January 13, 2001. Tendulkar, Suresh D. (2001: "Has Poverty in India Declined in 1990s?: An Analysis of Problems of Comparability Over Time", Paper presented at the International Seminar on "Understanding Socio-Economic Changes Through National Surveys", organised by the National Sample Survey Organisation, 12-13 May 2001, New Delhi.